The laboratory, applied.

Every year, the laboratory publishes whitepapers, open source code, and the Impact Report. But a significant part of our work never ends up in a paper — it ends up in production, inside companies with a problem that isn't solved by buying a product off the shelf. Sometimes the problem precedes the research, and the path reverses — we start from an AI system that needs stabilizing, a process to transform, an agent to move from prototype to deployment. This page is about that part of the laboratory: who reaches out, with what problems, how we work together.

Every research stream we explore has an applied counterpart. Distributed AI — the way we design algorithms that run from the industrial server to the edge device — becomes, in production, the design of agentic systems: architectures where multiple models work together, coordinated, each with a precise and verifiable task.

When these architectures must live alongside data that cannot leave the company, federated learning comes into play: models that train on the data where it lives, without centralizing it. This is sovereign on-premise AI — on-prem when needed, hybrid when it makes sense. Where appropriate, fine-tuning models on proprietary company data integrates into the same flow.

The vector databases and semantic memory we study find application in LLM integration with enterprise knowledge — reliable RAG over technical documents, procedures, decision history. Here AI engineering is not a chat panel glued onto a database; it's a system that understands, verifies, and answers with precision.

The work on multi-AI orchestration — which in Reasonance we rendered visual and in VIBE Framework we codified as discipline — translates into systems where multiple models work side by side, with traceable policy, permissions, and audit.

And where AI already exists in the company but struggles — prototypes that don't scale, runaway costs, codebases that need stabilizing — our approach is senior AI engineering: we read the code, identify what needs rebuilding and what should be preserved, and work closely with the internal team.

Three kinds of people, usually, end up in our contact form.

The first leads a mature organization — manufacturing, energy, finance, pharma — with decades of systems that work, technical documentation of value, and the awareness that "introducing AI" doesn't mean rebuilding everything. Often a founder, an administrator, a director who needs a partner capable of understanding the legacy before proposing AI on top of it. Not looking for a showcase POC; looking for systems that go to production and stay there.

The second is the technical lead — CTO, Head of Engineering, R&D director — of a company that is already using AI and sees its fragilities. Prototypes that don't become products, runaway LLM costs, agents that work in demo and fail in production, codebases that need stabilizing. Here the work is often engineering: auditing, architectural refoundation, introducing disciplines that didn't yet exist when LLMs arrived.

The third is rarer but growing: the founder of an AI-native project who needs senior bandwidth on AI agent engineering or multi-model orchestration — skills that the job market struggles to supply, and that are impossible for an early-stage startup to hire full-time.

We don't have a sales team, and it's not a tagline. The messages that come through the contact form, we read ourselves. We usually answer within 3–5 business days, sometimes longer if we're deep in a research experiment.

The first contact is not a pitch. It's a research conversation: we try to understand what the actual problem is, whether we have the time and competence to take it on, and whether mutual expectations are compatible with how we work. If there's a match, we propose a project structure — often written as an internal mini-whitepaper. If there's no match, we say so.

We prefer multi-year engagements or projects with publishable output — because our business model holds together client work and research, and because we believe that whoever releases the standard defines it. We also collaborate with academic and technology partners, and with internal teams who want to learn the method as well as receive the result.